Design and Optimization of Indomethacin Nanocrystals using Machine Learning and Molecular Dynamics Simulations

In this study, a stable nanocrystalline drug delivery system for indomethacin (IND) was rapidly developed by integrating machine learning methods with Hummer Acoustic Resonance (HAR) technology. This system effectively enhanced the solubility of IND and demonstrated excellent scalability. High-throughput screening using HAR technology identified P188-PVA as the optimal composite stabilizer for IND nanocrystal suspensions. Molecular dynamics simulations were employed to thoroughly investigate the interaction mechanisms between the drug and stabilizers. Systematic design and optimization of IND nanocrystal formulation parameters and HAR process conditions were conducted using an integrated modeling approach combining Box-Behnken design (BBD) and artificial neural networks (ANN). Various statistical metrics were employed to evaluate and compare the predictive accuracy and generalization capability of BBD-RSM and ANN models, thereby identifying the optimal formulation. HAR technology was successfully used to scale up the optimal formulation by 5- and 50-fold, demonstrating its initial potential for scalability. Freeze-drying, spray-drying, and fluidized-bed drying techniques were evaluated for solidifying the prepared nanocrystal suspensions. Multiple analytical techniques were employed to characterize the particle size and solid-state properties of IND nanocrystals. PXRD and DSC analyses confirmed the crystalline nature of the IND nanocrystals. In vitro dissolution experiments indicated that IND nanocrystals exhibited significantly improved dissolution compared to raw IND. Additionally, the concepts and methodologies proposed in this study could also be applied to the development of other poorly water-soluble drugs.